deepinv.sampling#
This package contains various posterior sampling algorithms, including diffusion-based methods and MCMC methods. Please refer to the user guide for more details.
Diffusion models with Stochastic Differential Equations for Image Generation and Posterior Sampling#
User Guide: refer to Diffusion models for more information.
Base class for Stochastic Differential Equation (SDE):min_num_steps |
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Define the Reverse-time Diffusion Stochastic Differential Equation. |
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Variance-Exploding Stochastic Differential Equation (VE-SDE) |
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Variance-Preserving Stochastic Differential Equation (VP-SDE) |
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Posterior distribution sampling for inverse problems using diffusion models by Reverse-time Stochastic Differential Equation (SDE). |
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Preconditioned data fidelity term for noisy data \(- \log p(y|x + \sigma(t) \omega)\) with \(\omega\sim\mathcal{N}(0,\mathrm{I})\). |
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The DPS data-fidelity term. |
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Base class for solving Stochastic Differential Equations (SDEs) from |
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Euler-Maruyama solver for SDEs. |
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Heun solver for SDEs. |
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A container for storing the output of an SDE solver, that behaves like a |
Custom diffusion posterior samplers#
User Guide: refer to Popular posterior samplers for more information.
Denoising Diffusion Restoration Models (DDRM). |
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Diffusion PnP Image Restoration (DiffPIR). |
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Diffusion Posterior Sampling (DPS). |
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Turns a diffusion method into a Monte Carlo sampler |
Markov Chain Monte Carlo Langevin#
User Guide: refer to Markov Chain Monte Carlo for more information.
Base class for Monte Carlo sampling. |
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Projected Plug-and-Play Unadjusted Langevin Algorithm. |
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Plug-and-Play SKROCK algorithm. |